While the standard VAEs are proven to be powerful generative models for complex data modelling, they assume that the data samples are independent of each other.
To overcome this constraint, the i.i.d. standard Gaussian prior is replaced with a Gaussian process (GP) prior in recent work [1]}, [2]}, [3]}.
These GP prior VAEs can also utilise auxiliary covariates such as time, gender, and health condition. Thus, they can be used as conditional generative models.
